Recap of session 7

• Importing data with readr
• Where does your data live?
• Factors

File paths and working directories

• A character string that tells you the location of a file
• Absolute path: starts from the “root” directory
  • e.g. /Users/elenatuz/Downloads/datafile.csv
• Relative path: starts from the current directory (denoted by .)
  • e.g. If I am in the folder /Users/elenatuz: ./Downloads/datafile.csv
  • e.g. If I am in the folder /Users/elenatuz/Downloads: ./datafile.csv or simply datafile.csv

File paths and working directories

• Working directory: where R looks for files that you ask it to load
• Also where R will put any files that you ask it to save
• You can see your current working directory at the top of the console or by typing getwd()
• You can change the working directory with setwd() function or Session > Set Working Directory > …

Factors

• A concept unique to R
• Useful for working with categorical variables: variables that have a fixed and known set of possible values
• Why use factor variables instead of character variables?
  • Character variables don’t protect you from typos
  • Character variables don’t sort in a useful way

Functions for factors

• fct_recode(): change factor levels
• fct_collapse() and fct_lump(): reduce the number of factor levels
• fct_infreq(): to sort factor levels by how often they appear
• fct_reorder(): to sort factor levels by some other variable
• fct_rev(): reverse the order of the factor levels

All these functions are part of the forcats package, which is automatically loaded when you load the tidyverse package.

Agenda for today

• Review!

RStudio

When you open RStudio, you should see something like this:

There should be 3 different windows along with a number of tabs.

RStudio

When you open RStudio, you should see something like this:

R console in Left: can run commands in an interactive fashion. Type a command and hit the Enter key.

RStudio

When you open RStudio, you should see something like this:

Environment in Top-right: list of objects that we have access to.

RStudio

When you open RStudio, you should see something like this:

Files in Bottom-right: allows you to navigate the directory structure on your computer.

RStudio

When you open RStudio, you should see something like this:

Plots in Bottom-right: any graphical output you make will be displayed here.

RStudio

When you open RStudio, you should see something like this:

Help in Bottom-right: documentation for functionName appears here when you type ?functionName in the console.

RStudio

When you open RStudio, you should see something like this:

Top-left: nothing so far, but potentially your R script or R markdown.

You can store your code in: R scripts

• An R script is a file containing lines of R code that are meant to be run altogether
• R scripts are typically working files, not intended for presentation
• R scripts have .R file extensions
• Comments can be inserted to explain the code

To create R script: click in the top-left corner of the window, and click “R Script”.

Types of information in R scripts

• Code

To execute the code: highlight the code and click at the top of the window (or Cmd-Enter on a Mac, Ctrl-Enter on Windows).

The output will appear in Console or Plots section.

You can store your code in: R markdown

• An R markdown is a document format which allows you to “weave together narrative text and code to produce elegantly formatted output.”
• Typically used for presentation
• R markdown files have .Rmd extensions
  
• R markdown cheatsheet and reference guide available here

To create R markdown: click in the top-left corner of the window, and click “R Markdown”.

Types of information in R markdown

• Text chunks
• Code chunks (grey area)

Code chunks in R markdown

To inserts a new code chunk: click Option-Cmd-I on a Mac or Ctrl-Alt-I on Windows.

To execute the code: click on the green arrow in the top-right corner of the code chunk.

The output will appear below the code chunk.

Common Rmd chunk options

• include = FALSE: prevents code and results from appearing in the finished file. R Markdown still runs the code in the chunk, and the results can be used by other chunks.
  • Useful for decluttering your Rmd output, showing only essential code.
• echo = FALSE: prevents code, but not the results from appearing in the finished file.
  • Useful if you just want to show figures but not code that generated it.
• eval = FALSE: Code appears in the output but is not run.
  • Useful for presenting code for demonstration purposes.
• message = FALSE: prevents messages that are generated by code from appearing in the finished file.
  • Useful for suppressing messages when loading packages.
• warning = FALSE: prevents warnings that are generated by code from appearing in the finished.
  • Useful for suppressing warnings when loading packages, plotting data or fitting models.

Text chunks in R markdown

Add your description outside the code chunk.

Can add:

• Headers
• Emphasis (italics, bold, strikethrough)
• Lists
• Links
• Images

Markdown reference here.

Rmd workflow (basic)

1. Edit .Rmd file in RStudio.
   
2. Knit the document (either by hitting the button or using a keyboard shortcut).
   • When you press “Knit”, the file is automatically saved.
     
   • Next, RStudio opens a new console, “knits” the document there, then closes that console. No code is run in your original console!
     
   • RStudio creates a .html file in the same folder as the .Rmd file.
3. Preview output in the preview pane, or by opening the .html file.
   • If you want to make changes, go back to Step 1.

Exercise 1

• Create a folder (e.g. on the Desktop)
• Create an R markdown
• Write something in the markdown: use both code and text options.
• Save it to your folder
• Knit it
• Check that .html file appeared in the folder

What is a variable?

• An “envelope” or “bucket” where information can be stored and referenced
• Each “bucket” has a name on the outside, contains information inside
  • Variable name: name of the bucket
  • Variable value: information on the inside
• Variable assignment: process of putting information inside a bucket

Basic objects in R: Vectors

• One-dimensional array whose entries are the same type
• Can be created using the c() function, or using the : shortcut

vec <- c("a", "b", "c")
vec

## [1] "a" "b" "c"

Basic objects in R: Vectors

• One-dimensional array whose entries are the same type
• Can be created using the c() function, or using the : shortcut
• Most of operations are applied element-wise!

vec <- 1:10
vec

##  [1]  1  2  3  4  5  6  7  8  9 10

vec*2-1

##  [1]  1  3  5  7  9 11 13 15 17 19

vec^2

##  [1]   1   4   9  16  25  36  49  64  81 100

Basic objects in R: Vectors

To extract a subset of elements by their indices, put a vector of indices in square brackets

vecsq <- vec^2
vecsq

##  [1]   1   4   9  16  25  36  49  64  81 100

Continuous chunk (all elements from 3 to 7)

vecsq[3:7] 

## [1]  9 16 25 36 49

Just some elements (elements 3 and 5)

vecsq[c(3,5)]

## [1]  9 25

All except (elements 3 and 5)

vecsq[-c(3, 5)]

## [1]   1   4  16  36  49  64  81 100

Basic objects in R: Matrices

Two-dimensional analogs of vectors

A <- matrix(1:12, nrow = 3, ncol = 4)
A

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

To extract a subset of elements put the row numbers then column number.

One element (1st row, 2nd column)

A[1, 2]

## [1] 4

One row (3rd row)

A[3, ]

## [1]  3  6  9 12

A block (1-3 rows and 1-3 cols)

A[1:3, 1:3]

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

Basic objects in R: Lists

• A collection of key-value pairs
• Keys are character strings, values can be anything!
• Values do not have to be of the same type for different keys
• Created with the list() function

cars <- list(make = "Honda", 
             models = c("Fit", "CR-V", "Odyssey"), 
             available = c(TRUE, TRUE, TRUE))

Basic objects in R: Lists

To extract parts of a list use [[ or $ notation to refer to a specific key-value pair

cars$make         

## [1] "Honda"

cars[["models"]]

## [1] "Fit"     "CR-V"    "Odyssey"

Basic objects in R: Data Frames

Data structure for storing datasets.

• First row: variable names
• Each subsequent row represents one observation
• Each column contains the values of that variable across observations

mtcars

Basic objects in R: Data Frames

Data frame is has a list structure.

mtcars$mpg

##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4

And a matrix structure.

mtcars[1,2]

## [1] 6

Basic objects in R: Data Frames

Useful commands:

• head() - print top rows
• View() - open the data table in a new tab
• dim(), ncol(), nrow() - check data dimensions
• names() - print variable (col) names
• summary() - check data summary (e.g. mean, max, meadian)
• ? - check data description (if it is from a package)

Exercise 2

Answer these questions about mtcars:

• What is the dimensions of mtcars?
• What is the meaning of wt variable?
• What is the minimum value of wt?

Add the answer to your R markdown (code + comments).

ggplot essential elements of graphics: data, geometries, aesthetics

ggplot essential elements of graphics: data, geometries, aesthetics

Examples of other aesthetics

Examples of other aesthetics

ggplot2 code

ggplot()

ggplot2 code

ggplot() +
    geom_histogram(data = df, mapping = aes(x = mpg))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot2 code

ggplot() +
    geom_boxplot(data = df, mapping = aes(x = cylinders, y = mpg))

ggplot2 code

ggplot() +
    geom_point(data = df, 
               mapping = aes(x = weight, y = mpg, col = cylinders),
               shape = 15)

Note that shape is outside the aesthetics: although it controls visual properties of the plot, it has nothing to do with the data. Aesthetics links your data to the plot visual properties.

Two types of variables in data frames

• Continuous variable: Variable takes on values which fall on the real number line (or part of it)
  • E.g. height, exam score, attendance count
• Categorical variable: Variable takes on values which fall into discrete categories
  • E.g. ice-cream flavor, country of origin

Why do we care? It helps you to choose the plot type.

• 1 categorical variable: barplot
• 1 continuous variable: histogram
• Continuous vs. continuous: scatterplot
• Continuous vs. time: lineplot
• Continuous vs. categorical: boxplots & violin plots
• Categorical vs. categorical: heatmap

Barplots: counts for a categorical variable

What is the distribution of cylinders in my dataset?

ggplot() +
    geom_bar(data = df, mapping = aes(x = cylinders)) +
    ggtitle("Count by cylinders") +
    xlab("No. of cylinders")

Note that y is automatically set to counts.

Histograms: counts for a continuous variable

What is the distribution of miles per gallon in my dataset?

ggplot() + 
    geom_histogram(data = df, mapping = aes(x = mpg)) +
    ggtitle("Histogram of miles per gallon")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Scatterplots: continuous variable vs. continuous variable

What is the relationship between mpg and weight?

ggplot() + 
    geom_point(data = df, mapping = aes(y = mpg, x = weight), size = 2) + 
    ggtitle("Miles per gallon vs. weight")

Lineplots: continuous variable vs. time variable

What is the relationship between mpg and time?

ggplot() +
    geom_line(data = vehicles, mapping = aes(y = `mean highway mpg`, x = year)) +
    ggtitle("Mean highway mpg by year")

Easier to see the trend.

Boxplots & violin plots: continuous variable vs. categorical variable

For each value of cylinder, what is the distribution of mpg like?

ggplot() + 
    geom_boxplot(data = df, aes(cylinders, mpg)) +
    ggtitle("Distribution of mpg by cylinders")

ggplot() + 
    geom_violin(data = df, aes(cylinders,mpg)) +
    ggtitle("Distribution of mpg by cylinders")

Heatmaps: categorical variable vs. categorical variable

How often does each pair of cylinder and gear occur in the dataset?

ggplot() + 
    geom_tile(data = df, mapping = aes(y = gear, x = cylinders, fill = count)) + 
    ggtitle("Distribution of (cylinder, gear)")

Layers: Combining multiple plots into one graphic

We can have more than one layer in a graphic.

= +

Each layer contains (essentially):

• 1 dataset, 1 geometric object, aesthetic mappings

ggplot2 code

ggplot() +
    geom_boxplot(data = df, mapping = aes(x = cylinders, y = mpg)) +
    geom_point(data = df, mapping = aes(x = cylinders, y = mpg), 
               position = "jitter")

ggplot2 code

When layers share attributes, we only have to type them once:

ggplot(data = df, mapping = aes(x = cylinders, y = mpg)) +
    geom_boxplot() +
    geom_point(position = "jitter")

ggplot2 code

• We can drop data = if it is the first argument of ggplot()
• We can drop mapping = if:
  • it is the second argument of ggplot()
  • it is the first argument of a geom_xx() function

ggplot(df, aes(x = cylinders, y = mpg)) +
    geom_boxplot() +
    geom_point(position = "jitter")

Facets

• Plotting different parts of our data on different canvases
• Can give a clearer, less cluttered picture
• We can facet by rows and/or columns

ggplot(data = df, mapping = aes(y = mpg, x = weight)) + 
  geom_point(aes(col = cylinders), size = 2) + 
  facet_wrap(~cylinders, ncol = 1)

Facets

• Plotting different parts of our data on different canvases
• Can give a clearer, less cluttered picture
• We can facet by rows and/or columns

ggplot(data = df, mapping = aes(y = mpg, x = weight)) + 
  geom_point(aes(col = cylinders), size = 2) + 
  facet_grid(gear~cylinders, labeller = label_both)

Other useful options

• ggtitle(), xlab(), ylab() - change the title, and axes names
• theme_minimal(), theme_classic(), theme_dark() - change the plot background
• scale_x_continuous(), scale_y_continuous() - change the axes range
• scale_color_brewer() - select the color palette
• many more

Exersise 3

In the mtcars data:

• Which variables are categorical, which are continuous?
• Create a plot with at least two geom layers.
• Create a plot with facets.
• Add title, rename axes, change the theme

Add the answer to your R markdown (code + comments). (note that in the above code I applied some preprocessing to the data, check ?mtcars to see the description of the original data).

Transforming data with dplyr

• select(): pick variables/columns by their names
• mutate(): create new variables/columns based on existing ones
• arrange(): reorder rows, use arrange(desc()) to reoreder in descending fashion
• filter(): pick rows by their values
• summarize(): collapse many rows down to a single summary
• group_by(): perform operations at a group level

See lectures 5-6 for details.

Exersise 4

For the mtcars data:

• Write a dplyr statement with at least 3 pipes %>%. Explain what it does.
• Write a statement with both group_by() and summarise(). Explain what it does.

Add the answer to your R markdown (code + comments).